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NASA NTRS2022

Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model

Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 2012 July 12 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using the graduated cylindrical shell (GCS) model and the reconnected flux in posteruption arcades. We correctly reproduce all the magnetic field components of the CME at Earth, with an arrival time error of approximately 1 hr. We further estimate the average subjective uncertainties in the GCS fittings by comparing the GCS parameters of 56 CMEs reported in multiple studies and catalogs. We determined that the GCS estimates of the CME latitude, longitude, tilt, and speed have average uncertainties of 5°.74, 11°.23, 24°.71, and 11.4%, respectively. Using these, we have created 77 ensemble members for the 2012 July 12 CME. We found that 55% of our ensemble members correctly reproduce the sign of the magnetic field components at Earth. We also determined that the uncertainties in GCS fitting can widen the CME arrival time prediction window to about 12 hr for the 2012 July 12 CME. On investigating the forecast accuracy introduced by the uncertainties in individual GCS parameters, we conclude that the half-angle and aspect ratio have little impact on the predicted magnetic field of the 2012 July 12 CME, whereas the uncertainties in longitude and tilt can introduce relatively large spread in the magnetic field predicted at Earth.

Talwinder Singh↗
DOE OSTI2020

Power System Event Classification and Localization Using a Convolutional Neural Network

Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures

Proteins have been found to inhabit a diverse set of three-dimensional structures. The dynamics that govern protein interconversion between structures happen over a wide range of time scales-picoseconds to seconds. Our understanding of protein functions and dynamics is largely reliant upon our ability to elucidate physically populated structures. From an experimental structural characterization perspective, we are often limited to measuring the ensemble-averaged structure both in the steady-state and time-resolved regimes. Generating kinetic models and understanding protein structure–function relationships require atomistic knowledge of the populated states in the ensemble. Here, we present ensemble refinement methodologies that integrate time-resolved experimental signals with molecular dynamics models. We first discuss integration of experimental structural restraints to molecular models in disordered protein systems that adhere to the principle of maximum entropy for creating a complete set of ensemble structures. We then propose strategies to find kinetic pathways between the refined structures, using time-resolved inputs to guide molecular dynamics trajectories and the use of inference to generate tailored stimuli to prepare a desired ensemble of protein states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The effects of bias, drift, and trends in calculating anomalies for evaluating skill of seasonal-to-decadal initialized climate predictions

In initialized seasonal to decadal (S2D) predictions, model hindcasts rapidly drift away from the initial observed state and converge toward a preferred state characterized by systematic error, or bias. Bias and drift are among the greatest challenges facing initialized prediction today. Differences in trends between initial states and drifted states, combined with bias and drift, introduce complexities in calculating anomalies to assess skill of initialized predictions. We examine several methods of calculating anomalies using the Decadal Prediction Large Ensemble (DPLE) using the Community Earth System Model (CESM) initialized hindcasts and focus on Pacific and Atlantic SSTs to illustrate issues with anomaly calculations. Three methods of computing anomalies, one as differences from a long term model climatology, another as bias-adjusted differences from the previous 15 year average from observations, and a third as differences from the previous 15 year average from the model, are contrasted and each is shown to have limitations. For the first, trends in bias and drift introduce higher skill estimates earlier and later in the hindcast period due to the trends that contribute to skill. For the second, higher skill can be introduced in situations where low frequency variability in the observations is large compared to the hindcasts on timescales greater than 15 years, while lower skill can result if the predicted signal is small and the bias-correction itself produces a transition of SST anomalies to the opposite sign of those that are observed. The third method has somewhat lower skill compared to each of the others, but has less difficulties with not only the long term trends in the model climatology, but also with the unrealistic situational skill from using observations as a reference. However, the first 15 years of the hindcast period cannot be evaluated due to having to wait to accumulate the previous 15 year model climatology before the method can be applied. Here, the IPO transition in the 2014–2016 time frame from negative to positive (predicted by Meehl et al. in in Nat Commun, 10.1038/NCOMMS11718, 2016) did indeed verify using all three methods, though each provides somewhat different skill values as a result of the respective limitations. There is no clear best method, as all are roughly comparable, and each has its own set of limitations and caveats. However, all three methods show generally higher overall skill in the AMO region compared to the IPO region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Examining the Robustness of Weakened Orographic Influence on Precipitation in Downscaled Climate Projections Over the Western US

Assessing local climate change impacts often requires downscaling coarse global climate model (GCM) output to finer resolution. Two main approaches exist: dynamical downscaling using high-resolution regional climate models, and statistical downscaling based on historical relationships between large-scale and local variables. In a recent analysis of five dynamically downscaled simulations over the western United States, Koszuta et al. (2024, https://doi.org/10.1029/2023gl107298) found that warming weakens orographic influence on winter precipitation, damping increases on windward slopes and amplifying them in rain-shadowed regions. Here we show that this effect is robust across seasons and multiple dynamically downscaled ensembles, and is more pronounced at higher model resolutions. However, it is absent in projections from a widely used statistical model (LOCA2), even when trained on high-resolution future simulations (LOCA2-Hybrid). This highlights a key limitation of many statistical downscaling methods: their preservation of parent GCM trends, which usually fail to capture emergent changes in orographic precipitation patterns.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Winter QPF Sensitivities to Snow Parameterizations and Comparisons to NASA CloudSat Observations

Steady increases in computing power have allowed for numerical weather prediction models to be initialized and run at high spatial resolution, permitting a transition from larger scale parameterizations of the effects of clouds and precipitation to the simulation of specific microphysical processes and hydrometeor size distributions. Although still relatively coarse in comparison to true cloud resolving models, these high resolution forecasts (on the order of 4 km or less) have demonstrated value in the prediction of severe storm mode and evolution and are being explored for use in winter weather events . Several single-moment bulk water microphysics schemes are available within the latest release of the Weather Research and Forecast (WRF) model suite, including the NASA Goddard Cumulus Ensemble, which incorporate some assumptions in the size distribution of a small number of hydrometeor classes in order to predict their evolution, advection and precipitation within the forecast domain. Although many of these schemes produce similar forecasts of events on the synoptic scale, there are often significant details regarding precipitation and cloud cover, as well as the distribution of water mass among the constituent hydrometeor classes. Unfortunately, validating data for cloud resolving model simulations are sparse. Field campaigns require in-cloud measurements of hydrometeors from aircraft in coordination with extensive and coincident ground based measurements. Radar remote sensing is utilized to detect the spatial coverage and structure of precipitation. Here, two radar systems characterize the structure of winter precipitation for comparison to equivalent features within a forecast model: a 3 GHz, Weather Surveillance Radar-1988 Doppler (WSR-88D) based in Omaha, Nebraska, and the 94 GHz NASA CloudSat Cloud Profiling Radar, a spaceborne instrument and member of the afternoon or "A-Train" of polar orbiting satellites tasked with cataloguing global cloud characteristics. Each system provides a unique perspective. The WSR-88D operates in a surveillance mode, sampling cloud volumes of Rayleigh scatterers where reflectivity is proportional to the sixth moment of the size distribution of equivalent spheres. The CloudSat radar provides enhanced sensitivity to smaller cloud ice crystals aloft, as well as consistent vertical profiles along each orbit. However, CloudSat reflectivity signatures are complicated somewhat by resonant Mie scattering effects and significant attenuation in the presence of cloud or rain water. Here, both radar systems are applied to a case of light to moderate snowfall within the warm frontal zone of a cold season, synoptic scale storm. Radars allow for an evaluation of the accuracy of a single-moment scheme in replicating precipitation structures, based on the bulk statistical properties of precipitation as suggested by reflectivity signatures.

Molthan, Andrew↗
DOE OSTI2020

Sensitivity Study for Forecasting Variables of WRF-Solar Using a Tangent Linear Approach

Integrating solar generation in recent years has highlighted the need for improved accuracy in predicting solar power. Confidence in solar power forecasting can be achieved by designing an ensemble that provides reliable probabilistic information for solar radiation with reduced uncertainty and error. Ideally, ensemble members are created through the optimized perturbation of the initial conditions in numerical weather prediction (NWP) models. Tangent linear models are capable of efficiently investigating the sensitivity of solar radiation to model input parameters because they do not require individual perturbation of each variable. This sensitivity study using tangent linear models provide us the capability to identify the right variables to perturb in an ensemble prediction system. In this study, we developed tangent linear models for WRF-Solar modules that directly impact the computation of solar radiation and the simulation of cloud formation and dissipation including the Fast All-sky Model for Solar Applications (FARMS), the Noah land surface model (LSM), the Thompson microphysics, the Mello-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization, and the Deng scheme for a shallow-convection parameterization. A sensitivity analysis was conducted under various scenarios based on satellite observations and model simulations from the National Solar Radiation Data Base (NSRDB) and WRF-Solar, respectively. Critical forecasting variables that are highly sensitive to the forecasting of global horizontal irradiance (GHI), direct normal irradiance (DNI), cloud mixing ratio, cloud tendency, cloud fraction, and sensible and latent heat fluxes were determined using the relevant WRF-Solar module. This study will be used as a guidance on future research leading to high-quality probabilistic solar forecasting. In this presentation, we discuss the validation of tangent linear approach for WRF-Solar modules and illustrate how the sensitivity results are valuable in the improvement of probabilistic solar prediction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Deep learning model for fast, science-based forecasting of fluid migration along faults in geologic carbon storage scenarios

Effective long-term geologic storage depends on robust site selection and credible, science-based forecasting of subsurface behavior to ensure storage integrity. For this work, we develop a deep learning–based reduced-order model (ROM) to quantify potential carbon dioxide (CO₂) and brine migration through geological faults. The ROM combines a Transformer model for binary classification and a Stacked Ensemble for regression, trained on a comprehensive dataset generated from 1400 physics-based reservoir simulations. Key geologic and operational parameters—including fault geometry, reservoir structure, and injection conditions—were systematically varied to capture a wide range of fluid migration scenarios. The ROM accurately predicts the onset of migration, cumulative migration volumes of both CO₂ and brine, and associated migration rates, as compared to an independent set of validation simulations, while significantly reducing computational cost compared to traditional simulation methods. Model performance was evaluated across diverse fault configurations, revealing that shallow reservoir geometry and fault angle are among the most influential factors governing migration behavior. Sensitivity analysis using SHapley Additive exPlanations (SHAP) provided interpretability, revealing distinct patterns in how geological and operational features drive transient versus cumulative migration outcomes. The ROM’s ability to rapidly simulate fault migration scenarios enables efficient sensitivity analyses, scenario evaluations, and decision support for site selection and monitoring design. This approach enhances the safety, scalability, and long-term operational performance of geologic carbon storage (GCS) systems by providing a robust, interpretable tool for predicting subsurface fluid migration and assessing fault-related migration potential.

42 ENGINEERING↗
DOE OSTI2020

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Abstract This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Simulation of evapotranspiration and yield of maize: An inter-comparison among 41 maize models

Accurate simulation of crop water use (evapotranspiration, ET) can help crop growth models to assess the likely effects of climate change on future crop productivity, as well as being an aid for irrigation scheduling for today's growers. To determine how well maize (Zea mays L.) growth models can simulate ET, an initial inter-comparison study was conducted in 2019 under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Herein, we present results of a second inter-comparison study of 41 maize models that was conducted using more comprehensive datasets from two additional sites - Mead, Nebraska, USA and Bushland, Texas, USA. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. ET was measured using eddy covariance at Mead and using large weighing lysimeters at Bushland. A wide range in ET rates was simulated among the models, yet several generally were able to simulate ET rates adequately. The ensemble median values were generally close to the observations, but a few of the models sometimes performed better than the median. Many of the models that did well at simulating ET for the Mead site did poorly for drier, windy days at the Bushland site, suggesting they need to improve how they handle humidity and wind. Additional variability came from the approaches used to simulate soil water evaporation. Fortunately, several models were identified that did well at simulating soil water evaporation, canopy transpiration, biomass accumulation, and grain yield. These models were older and have been widely used, which suggests that a larger number of users have tested these models over a wider range of conditions leading to their improvement. These revelations of the better approaches are leading to model improvements and more accurate simulations of ET.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2006

An Ensemble-Based Smoother with Retrospectively Updated Weights for Highly Nonlinear Systems

Monte Carlo computational methods have been introduced into data assimilation for nonlinear systems in order to alleviate the computational burden of updating and propagating the full probability distribution. By propagating an ensemble of representative states, algorithms like the ensemble Kalman filter (EnKF) and the resampled particle filter (RPF) rely on the existing modeling infrastructure to approximate the distribution based on the evolution of this ensemble. This work presents an ensemble-based smoother that is applicable to the Monte Carlo filtering schemes like EnKF and RPF. At the minor cost of retrospectively updating a set of weights for ensemble members, this smoother has demonstrated superior capabilities in state tracking for two highly nonlinear problems: the double-well potential and trivariate Lorenz systems. The algorithm does not require retrospective adaptation of the ensemble members themselves, and it is thus suited to a streaming operational mode. The accuracy of the proposed backward-update scheme in estimating non-Gaussian distributions is evaluated by comparison to the more accurate estimates provided by a Markov chain Monte Carlo algorithm.

Monte Carlo↗
NASA NTRS2021

Satellite Constraints on the Latitudinal Distribution and Temperature Sensitivity of Wetland Methane Emissions

Wetland methane (CH4) emissions comprise about one-third of the global CH4 source. The latitudinal distribution and climate sensitivity of wetland CH4 fluxes are the key determinants of the global CH4-climate feedback. However, large differences exist between bottom-up estimates, informed by ground-based flux measurements, and top-down estimates derived from spaceborne total column CH4. Despite the extensive coverage of satellite CH4 concentration observations, challenges remain with using top-down estimates to test bottom-up models, mainly because of the uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, and the confounding role of the posterior error covariance structures. Here, we use satellite-based top-down CH4 flux estimates (2010–2012) to test and refine 42 bottom-up estimates of wetland emissions that use a range of hypothesized wetland extents and process controls. Our comparison between bottom-up models and satellite-based fluxes innovatively accounts for cross-correlations and spatial uncertainties typically found in top-down inverse estimates, such that only the information from satellite observations and the atmospheric transport model is kept as a constraint. We present a satellite-constrained wetland CH4 ensemble product derived from assembling the highest-performance bottom-up models, which estimates global wetland CH4 emissions of 148 (117–189, 5th–95th percentile) Tg CH4 yr−1. We find that tropical wetland emissions contribute 72% (63%–85%) to the global wetland total. We also find that a lower-than-expected temperature sensitivity agrees better with atmospheric CH4 measurements. Overall, our approach demonstrates the potential for using satellites to quantitatively refine bottom-up wetland CH4 emission estimates, their latitudinal distributions, and their sensitivity to climate.

wetland ch4↗
DOE OSTI2022

Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters

The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time domain survey cadences and the training of classification models on data sets with few to no labels. Traditional data augmentation techniques expand training sets by reenvisioning observed exemplars, seeking to simulate observations of specific training sources under different (exogenous) conditions. Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation. Moreover, the principal drawback of theory-driven models lies in the prohibitive computational cost of simulating source observables from ab initio parameters. In this work, we propose a computationally tractable machine learning approach to generate realistic light curves of periodic variables capable of integrating physical parameters and variability classes as inputs. Our deep generative model, inspired by the transparent latent space generative adversarial networks, uses a variational autoencoder (VAE) architecture with temporal convolutional network layers, trained using the OGLE-III optical light curves and physical characteristics (e.g., effective temperature and absolute magnitude) from Gaia DR2. A test using the temperature–shape relationship of RR Lyrae demonstrates the efficacy of our generative “physics-enhanced latent space VAE” (PELS-VAE) model. Such deep generative models, serving as nonlinear nonparametric emulators, present a novel tool for astronomers to create synthetic time series over arbitrary cadences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Predictive analytics to direct clinical attention to complex patients with elevated suicide risk: enhancement of the Veterans Health Administration REACH VET model

Suicide is a major public health concern, particularly among Veterans. The U.S. Department of Veterans Affairs Veterans Health Administration (VHA) employs the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) model to prioritise high-risk patients for targeted clinical attention. REACH VET 1.0 (RV 1.0) was developed on 2008–2011 data. To reflect changes in clinical practice and populations, VHA updated it to REACH VET 2.0 (RV 2.0). This study describes its development and validation. RV 2.0 used longitudinal data from 7,248,170 VHA patients (4,967 suicide deaths) in 2018–2019, with 650 time-varying demographic, clinical and area-level predictors derived from a 2-year lookback (2016–2019). An ensemble of Elastic-Net logistic regression models was trained on 2018 data and evaluated monthly at the population level in 2019, focusing on the top 0.1% intervention risk tier. Analyses assessed model discrimination, suicide detection, risk concentration, subgroup consistency (sex, age and race/ethnicity) and performance relative to RV 1.0 using the same percentile-based risk strata. RV 2.0 outperformed RV 1.0 across all risk strata, with better discrimination (C-statistic 0.76 vs 0.69) and consistent performance across demographic subgroups. Within the top 0.1% of predicted risk, RV 2.0 identified more deaths, higher suicide rates and greater mortality risk concentration both when averaged across the 12 monthly 2019 test sets (5.6 vs 3.6; 83.6 vs 53.7 per 100,000 person-years; 21.0 vs 14.1) and when annualised for 2019 (67 vs 43; 2.7% vs 1.7%; 1,003 vs 644 per 100,000 person-years; 26.7 vs 17.1). RV 2.0 improves suicide risk stratification among Veterans, demonstrating better performance and consistent prediction across subgroups and highlighting the need for regular model updates and evaluation.

Peluso, Alina [Oak Ridge National Laboratory (ORNL↗
NASA NTRS2003

Project trades model for complex space missions

A Project Trades Model (PTM) is a collection of tools/simulations linked together to rapidly perform integrated system trade studies of performance, cost, risk, and mission effectiveness. An operating PTM captures the interactions between various targeted systems and subsystems through an exchange of computed variables of the constituent models. Selection and implementation of the order, method of interaction, model type, and envisioned operation of the ensemble of tools rpresents the key system engineering challenge of the approach. This paper describes an approach to building a PTM and using it to perform top-level system trades for a complex space mission. In particular, the PTM discussed here is for a future Mars mission involving a large rover.

web services↗
DOE OSTI2025

The Influence of African Easterly Waves on Atlantic Tropical Cyclone Tracks and Landfall in Large Ensembles

African easterly waves (AEWs) are an important precursor or “seed” for Atlantic tropical cyclones (TCs), with 60%–80% of major hurricanes observed to originate from AEWs. However, climate model simulations indicate that AEWs are not necessary to maintain annual Atlantic TC frequency. Furthermore, small ensembles suggest that AEWs may impact the spatial distribution and landfall of Atlantic TCs. Here, we investigated the influence of AEWs on the spatial distribution of Atlantic TC tracks and landfall using 50-member ensembles of TC-permitting regional model simulations for five hurricane seasons characterized by different levels of TC activity. The control simulations are seasonal hindcasts in which AEWs were prescribed through the eastern lateral boundary condition using reanalysis. In the experiments, we suppressed AEWs by applying a 2–10 day filter to the eastern lateral boundary condition. In response to AEW suppression, we discovered statistically significant increases in Atlantic TC frequency (10%–26%) and landfall (16%–44%), a westward shift in TC genesis location and tracks with increased landfall over the Caribbean Islands, southwestern Gulf Coast, and southeastern US coast, and increases in mid-tropospheric relative humidity in the main development region. In addition, we evaluated TC genesis mechanisms in the absence of AEWs and found evidence that the intertropical convergence zone (ITCZ) intensified and extended northward, resulting in ITCZ wave-breaking that shed vortices which served as TC seeds. By uncovering the connections between TC seed types and the likelihood of TC landfall, this research can provide Atlantic coastal and island communities with useful information to prepare for TC impacts.

African easterly waves↗
DOE OSTI2023

Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity

Abstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.

Meteorology & Atmospheric Sciences↗